Even as organizations adopt increasingly powerful LLMs, they will find it difficult to shed their reliance on humans.
By Peter Cappelli, Prasanna (Sonny) Tambe, and Valery Yakubovich
Large language models (LLMs) are a paradigm-changing innovation in data science. They extend the capabilities of machine learning models to generating relevant text and images in response to a wide array of qualitative prompts. While these tools are expensive and difficult to build, multitudes of users can use them quickly and cheaply to perform some of the language-based tasks that only humans could do before.
This raises the possibility that many human jobs — particularly knowledge-intensive jobs that primarily involve working with text or code — could be replaced or significantly undercut by widespread adoption of this technology. But in reality, LLMs are much more complicated to use effectively in an organizational context than is typically acknowledged, and they have yet to demonstrate that they can satisfactorily perform all of the tasks that knowledge workers execute in any given job.
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(Outlet: MIT Sloan Management Review)
